CCF-BSF:CIF: Small: Coding for Fast Storage Access and In-Memory Computing

第1部分：在移动和云计算需求的推动下，数据存储需求呈现出急剧增长的趋势，不仅朝着更高的存储密度方向发展，同时还在访问性能方面实现了雄心勃勃的提升。由访问挑战驱动的一个相关的令人兴奋的新兴趋势是内存中计算，即将计算从主处理单元卸载到内存，以减少传输时间和能量。传统存储体系结构不可避免地以可靠性和容量换取延迟，无法解决未来快速存储访问和内存计算的挑战。这一瓶颈要求创新的研究成果能够同时最大限度地提高存储密度、访问性能和计算功能。该项目通过开发原则性的数学基础来解决这一迫在眉睫的挑战，这些基础将支持未来的计算系统，这些系统具有解决新的数据密集型应用所必需的质量，重点是基本的性能界限、算法和实用的信道编码方法。该项目的成果将在现代数据驱动和机器学习应用方面得到展示，将推动信息科学中的数学技术，并将直接影响未来的计算机系统结构，以满足日益增长和广泛的社会和科学对计算和快速数据处理的需求。此外，该提案提供了几种产生更广泛影响的机制，包括通过首席研究员在加州大学洛杉矶分校领导的现有研究中心与数据存储和存储行业的接触，课程开发和在加州大学洛杉矶分校工程在线硕士课程中引入新的研究生课程，本科生研究人员的参与，以及通过调查风格的文章和教程传播结果。第二部分：该项目有以下三个互补的研究目标：1)发明新的信道码，用于延迟敏感应用的可靠和快速存储器访问，研究跨越一般存储器和特别是阻性存储器的具体方案。所提出的方案将提供对振动编码主题的非平凡扩展：具有局部性(代数和基于图的)的编码和约束编码；2)发明新的信道码，其解码直接在存储器中执行，以使得能够同时满足在等待时间和可靠性方面的竞争要求。在这里，解码器本身受到计算误差的影响，它们本身表现为数据相关的意义。该分析将导致对数据相关错误稳健的界限和实用的代码设计。一个范例将是使用空间耦合设计的代码和使用加窗消息传递解码器解码的代码；3)为稳健的内存计算开发新的基本界限、算法和通道代码，重点是量化统计推理中的计算基元和现代数据驱动应用程序中使用的其他机器学习算法的健壮性。这些措施包括基本的性能限制和基于编码的新方法，以同时对抗偷袭路径和计算噪音。分析将包括编码(嘈杂的)海明/欧几里德相似性计算，在实际机器学习应用的背景下进行评估。该项目的结果也将有助于加州大学洛杉矶分校的课程开发，并将为来自代表性不足群体的本科生研究人员提供新的机会。

Part 1:Driven by the needs of mobile and cloud computing, demand for data storage is exhibiting steep growth, both in the direction of higher storage density as well as a simultaneous ambitious increase in access performance. A related exciting emerging trend driven by access challenges is in-memory computing, whereby computations are offloaded from the main processing units to the memory to reduce transfer time and energy. The challenges of future rapid storage access and in-memory computing cannot be addressed by the conventional storage architectures that inevitably trade off reliability and capacity for latency. This bottleneck calls for innovative research contributions that can simultaneously maximize the storage density, access performance, and computing functionality. This project addresses this imminent challenge by developing principled mathematical foundations that will underpin future computing systems possessing qualities necessary to address new data-intensive applications, focusing on fundamental performance bounds, algorithms, and practical channel coding methods. The results of this project will be demonstrated on modern data-driven and machine learning applications, will advance the repertoire of mathematical techniques in information sciences, and will directly impact future computer system architectures to meet the growing and wide ranging societal and scientific needs for computing and rapid data processing. Additionally, the proposal offers several mechanisms for broader impacts, including engagement with data storage and memory industry through the existing research center that the principal investigator is leading at UCLA, curriculum development and the introduction of new graduate courses in the UCLA on-line master's program in engineering, engagement of undergraduate researchers, and dissemination of the results through survey-style articles and tutorials.Part 2: The project has the following three complementary research goals:1) Invention of new channel codes for reliable and fast memory access for latency sensitive applications, with the study spanning general memories and specific schemes for resistive memories in particular. The proposed schemes will offer non-trivial extensions to vibrant coding subjects: codes with locality (algebraic and graph-based) and constrained coding; 2) Invention of new channel codes for which the decoding is performed directly in memory to enable simultaneously satisfying competing requirements on latency and reliability. Here, the decoder itself is subject to computational errors, themselves manifested in a data dependent sense. The analysis will lead to bounds and practical code designs robust to data-dependent errors. An exemplar will be codes designed using spatial coupling and decoded using windowed message passing decoders;3) Development of novel fundamental bounds, algorithms, and channel codes for robust in-memory computing, with the focus on quantifying the robustness of computing primitives in statistical inference and other machine learning algorithms used in modern data-driven applications. These include fundamental performance limits and new coding-based methods to simultaneously combat sneak paths and computing noise. Analysis will include coding for (noisy) Hamming/Euclidean similarity calculations, evaluated in the context of practical machine learning applications.Results from this project will also contribute to the curriculum development at UCLA and will offer new opportunities for the engagement of undergraduate researchers from underrepresented groups.